70 research outputs found
Network Dependency in Migration Flows – A Space-time Analysis for Germany since Re-unification
The contribution of this paper is to analyse the role of network interdependencies in a dynamic panel data model for German internal migration fl ows since re-unification. So far, a capacious account of spatial patterns in German migration data is still missing in the empirical literature. In the context of this paper, network dependencies are associated with correlations of migration flows strictly attributable to proximate flows in geographic space. Using the neoclassical migration model, we start from its aspatial specification and show by means of residual testing that network dependency eff ects are highly present. We then construct spatial weighting matrices for our system of interregional flow data and apply spatial regression techniques to properly handle the underlying space-time interrelations. Besides spatial extensions to the Blundell-Bond (1998) system GMM estimator in form of the commonly known spatial lag and unconstrained spatial Durbin model, we also apply system GMM to spatially filtered variables. Finally, combining both approaches to a mixed spatial filteringregression specification shows a remarkably good performance in terms of capturing spatial dependence in our migration equation and at the same time qualify the model to pass essential IV diagnostic tests. The basic message for future research is that space-time dynamics is highly relevant for modelling German internal migration flows.Internal migration, dynamic panel data; Spatial Durbin Model; GMM
Estimating Gravity Models of International Trade with Correlated Time-Fixed Regressors: To IV or not IV?
Gravity type models are widely used in international economics. In these models the inclusion of time-fixed regressors like geographical or cultural distance, language and institutional (dummy) variables is often of vital importance e.g. to analyse the impact of trade costs on internationalization activity. This paper analyses the problem of parameter inconsistency due to a correlation of the time-fixed regressors with the combined error term in panel data settings. A common solution is to use Instrumental-Variable (IV) estimation in the spirit of Hausman-Taylor (1981) since a standard Fixed Effect Model (FEM) estimation is not applicable. However, some potential shortcomings of the latter approach recently gave rise to the use of non-IV two-step estimators. Given their growing number of empirical applications, we aim to compare the performance of IV and non-IV approaches in the presence of time-fixed variables and right hand side endogeneity using Monte Carlo simulations, where we explicitly control for the problem of IV selection in the Hausman-Taylor case. The simulation results show that the Hausman-Taylor model with perfect-knowledge about the underlying data structure (instrument orthogonality) has on average the smallest bias. However, compared to the empirically relevant specification with imperfect-knowledge and instruments chosen by statistical criteria, simple non-IV rival estimators performs equally well or even better. We illustrate these findings by estimating gravity type models for German regional export activity within the EU. The results show that the HT specification tends to overestimate the role of trade costs proxied by geographical distance.Gravity model, Exports, Instrumental variables, two-step estimators, Monte Carlo simulations
Within and Between Panel Cointegration in the German Regional Output-Trade-FDI Nexus
For spatial data with a sufficiently long time dimension, the concept of global cointegration has been recently included in the econometrics research agenda. Global cointegration arises when non-stationary time series are cointegrated both within and between spatial units. In this paper, we analyze the role of globally cointegrated variable relationships using German regional data (NUTS 1 level) for GDP, trade, and FDI activity during the period 1976–2005. Applying various homogeneous and heterogeneous panel data estimators to a Spatial Panel Error Correction Model (SpECM) for regional output growth allows us to analyze the short- and long-run impacts of internationalization activities. For the long-run cointegration equation, the empirical results support the hypothesis of export- and FDI-led growth. We also show that for export and outward FDI activity positive cross-regional eff ects are at work. Likewise, in the short-run SpECM specification, direct and indirect spatial externalities are found to be present. As a sensitivity analysis, we use a spatial weighting matrix based on interregional goods transport fl ows rather than geographical distances. This scheme thus allows us to address more soundly the role of positive and negative effects of trade/FDI on output activity for a system of interconnected regions.Cointegration; Spatial Durbin model; growth; trade; FDI
Endogeneity in Panel Data Models with Time-Varying and Time-Fixed Regressors: To IV or not IV?
We analyse the problem of parameter inconsistency in panel data econometrics due to the correlation of exogenous variables with the error term.A common solution in this setting is to use Instrumental-Variable (IV) estimation in the spirit of Hausman-Taylor (1981). However, some potential shortcomings of the latter approach recently gave rise to the use of non-IV two-step estimators. Given their growing number of empirical applications, we aim to systematically compare the performance of IV and non-IV approaches in the presence of time-fixed variables and right hand side endogeneity using Monte Carlo simulations, where we explicitly control for the problem of IV selection in the Hausman-Taylor case. The simulation results show that the Hausman- Taylor model with perfect-knowledge about the underlying data structure (instrument orthogonality) has on average the smallest bias. However, compared to the empirically relevant specification with imperfect-knowledge and instruments chosen by statistical criteria, the non-IV rival performs equally well or even better especially in terms of estimating variable coefficients for timefixed regressors. Moreover, the non-IV method tends to have a smaller root mean square error (rmse) than both Hausman-Taylor models with perfect and imperfect knowledge about the underlying correlation between r.h.s variables and residual term.This indicates that it is generally more efficient.The results are roughly robust for various combinations in the time and cross-section dimension of the data.Endogeneity, instrumental variables, two-step estimators, Monte Carlo simulations
Estimating Gravity Models of International Trade with Correlated Time-Fixed Regressors: To IV or not IV?
Gravity type models are widely used in international economics. In these models the inclusion of time-fi0xed regressors like geographical or cultural distance, language and institutional (dummy) variables is often of vital importance e.g. to analyse the impact of trade costs on internationalization activity. This paper assesses the problem of parameter inconsistency due to a correlation of the time-fixed regressors with the combined error term in panel data settings. A common solution is to use Instrumental-Variable (IV) estimation in the spirit of Hausman-Taylor (1981) since a standard Fixed Effect Model (FEM) estimation is not applicable. However, some potential shortcomings of the latter approach recently gave rise to the use of non-IV two-step estimators. Given their growing number of empirical applications, we aim to compare the performance of IV and non-IV approaches in the presence of time-fixed variables and right hand side endogeneity using Monte Carlo simulations, where we explicitly control for the problem of IV selection in the Hausman-Taylor case. The simulation results show that the Hausman-Taylor model with perfect-knowledge about the underlying data structure (instrument orthogonality) has on average the smallest bias. However, compared to the empirically relevant specification with imperfect-knowledge and instruments chosen by statistical criteria, simple non-IV rival estimators performs equally well or even better. We illustrate these findings by estimating gravity type models for German regional export activity within the EU. The results show that the HT specification is likely to overestimate the role of trade costs proxied by geographical distance.Gravity model, Exports, Instrumental variables, two-step estimators, Monte Carlo.
Within and Between Panel Cointegration in the German Regional Output-Trade-FDI Nexus
For spatial data with a sufficiently long time dimension, the concept of global cointegration has been recently included in the econometrics research agenda. Global cointegration arises when non-stationary time series are cointegrated both within and between spatial units. In this paper, we analyze the role of globally cointegrated variable relationships using German regional data (NUTS 1 level) for GDP, trade, and FDI activity during the period 1976-2005. Applying various homogeneous and heterogeneous panel data estimators to a Spatial Panel Error Correction Model (SpECM) for regional output growth allows us to analyze the short- and long-run impacts of internationalization activities. For the long-run cointegration equation, the empirical results support the hypothesis of export- and FDI-led growth. We also show that for export and outward FDI activity positive cross-regional effects are at work. Likewise, in the short-run SpECM specification, direct and indirect spatial externalities are found to be present. As a sensitivity analysis, we use a spatial weighting matrix based on interregional goods transport flows rather than geographical distances. This scheme thus allows us to address more soundly the role of positive and negative effects of trade/FDI on output activity for a system of interconnected regions.
Evaluating EU Regional Policy: Many Empirical Specifications, One (Unpleasant) Result
Numerous studies have focused on the role of EU regional policy in fostering growth and convergence among European regions, why conducting another one? We argue that two facts are still lacking in the actual academic debate in order to get a sound empirical identification strategy and reliable results: First, one should take the theoretical underpinnings of regional growth models more serious, and second, a likewise careful account of the role of spatial dependence in the underlying data is needed. Though research has increasingly become aware of the latter point as important control factor for regional heterogeneity and omitted variables, in empirical operationalization still the ad-hoc inclusion of a hardly interpretable ‘catch-all’ spatial lag term of the endogenous variable is the first choice. We rather follow the lines of new theoretical and empirical approaches aiming at directly quantifying interregional spillovers associated with the amount of funds granted to lagging regions and their neighborhood. The dataset includes 127 NUTS1/-2 regions within the EU15 over the decade 1997-2007. In the spotlight of the investigation are the Objective 1 payments which are provided for lagging regions with a GDP p.c. of less than 75% of the EU average. These payments shall represent the main instrument to fulfill the central aim of European regional policy, the boost of convergence and harmonic growth over the EU. They represent about two third of the whole European cohesion policy. In our estimations we run a neoclassical convergence model in mainly four different specifications. On the one hand we separate in the aspatial and spatial models. On the other hand we run additive and multiplicative applications in order to consider the right coefficient interpretations. We estimate the model in various econometric specifications to point out the effectiveness of these funding. Our results all hint to the unpleasant result that EU structural funds objective 1 funding has a remarkably little or even negative direct impact on regional growth within the EU15. The spatial funding effects turn into negative significance in the most model specifications.
Trade, FDI and Cross-Variable Linkages: A German (Macro-)Regional Perspective
We analyse the evolution of German Trade and FDI activity within the EU27 using a simultaneous equation gravity approach for imports, exports, in- and outward FDI stocks based on German regional data (NUTS1-level) for 1993-2005. Our approach seeks to explore the main long-run driving forces of both trade/FDI and identify the likely linkages among them. Our motivation for a joint system estimation rests on the observation of a significant cross-equation residual correlation for single equation trade/FDI gravity models, which in turn opens up the possibility for enhancing estimation efficiency in a full information approach. 'On the fly' the simultaneous equation model also allows us to derive a measure for trade/FDI linkages based on the variance-covariance matrix of the system's error term. Adopting both a Hausman-Taylor (1981) IV approach (3SLS-GMM) and a rival non-IV estimator (the system extension to the Fixed Effects Vector Decomposition model recently proposed by Plümper \& Tröger, 2007) our main results are: We find empirical support for the chosen gravity setup as an appropriate framework in explaining German trade and FDI patterns with a prominent role given to trade costs (proxied by geographical distance). Looking at cross-variable linkages we find a substitutive link between trade (both ex-/imports) and outward FDI for the average of German states in line with earlier evidence for Germany, while imports and inward FDI are found complement each other. We also analyse the sensitivity of the results for regionally disaggregated sub-aggregates among the total pool of German state - EU27 country pairs. The results hint at structural differences among the trade and FDI activity of the two German Eastern and Western macro regions on the one hand, and also their interaction with the 'core' EU15 member states opposed to the overall EU27 aggregate on the other hand. Taking the West German - EU27 trade \& FDI relationship as an example, the identified pairwise linkages between the four variables closely follow the predictions of the New Trade theory model of Baldwin \& Ottaviano (2001): That is, when trade is merely of intra industry type with non-zero trade costs, the latter shift production abroad and lead to export replacement effects of FDI. However, at the same time outward FDI may stimulates trade via reverse good imports. For the West German - EU15 aggregate we even reveal complementaries among export and FDI activity, which have not been identified for German data before. This strongly advocates the importance of the regional dimension in analysing cross-variable linkages among trade and FDI.Trade, FDI, Panel Data, Simultaneous equations
Internal Migration, Regional Labour Market Dynamics and Implications for German East-West Disparities – Results from a Panel VAR
This paper analyses the causal linkages between regional labour market variables and internal migration flows among German states between 1991–2006. We adopt a Panel VAR approach to identify the feedback effects among the variables and analyse the dynamic properties of the system through impulseresponse functions.We also use the model to track the evolution of the particular East-West migration since re-unification aiming to shed more light on the East German “empirical puzzle”, characterized by lower migration responses than expected from the regional labour market position relative to the West. We indeed get evidence for such a puzzle throughout the mid-1990s, which is likely to be caused by huge West-East income transfers, a fast exogenously driven wage convergence and the possibility of East-West commuting. However, we also observe an inversion of this relationship for later periods:That is, along with a second wave of East-West movements around 2001 net flows out of East Germany were much higher than expected after controlling for its weak labour market and macroeconomic performance. Since this second wave is also accompanied by a gradual fading out of economic distortions, this supports the view of “repressed” migration flows for that period.Internal migration, Panel VAR, System GMM
Trade-FDI Linkages in a System of Gravity Equations for German Regional Data
We analyse the nature of German trade-FDI linkages within the EU27 based on a simultaneous equation gravity approach for imports, exports, in- and outward FDI stocks.We adopt both a Hausman-Taylor (1981) IV approach (3SLS-GMM) and rival non-IV estimation (the system extension to the Fixed Effects Vector Decomposition model recently proposed by Plümper & Tröger, 2007).Turning to the results, both estimators give empirical support for our chosen gravity setup as an appropriate framework in explaining German trade and FDI activity. Looking carefully at cross-variable linkages we basically find substitutive links between trade flows and outward FDI in line with earlier empirical evidence for Germany. Building upon German state level data we are also able to analyse the sensitivity of the results for regional sub-samples. The latter disaggregation hints at structural differences among the trade and FDI activity of the twoWest and East German macro regions on the one hand, and also their interaction with the ’core’ EU15 member states opposed to the overall EU27 aggregate on the other hand.TakingWest German–EU27 trade & FDI as an example, the identified pairwise linkages closely follow the theoretical predictions of New Trade Theory models as in Baldwin & Ottaviano (2001): That is, when trade is merely of intra-industry type with non-zero trade costs,we observe export replacement effects of FDI.However, at the same time outward FDI stimulates trade via reverse good imports. For the West German–EU15 sub-sample we even reveal complementaries among export and outward FDI activity.This strongly advocates to care for the regional dimension in analysing cross-variable linkages of trade and FDI.Trade, FDI, panel data, simultaneous equations
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